Housing Stress - SA2s in Greater Melbourne

Introduction

Calculating housing stress using 30/40 as in here: https://www.ahuri.edu.au/policy/ahuri-briefs/2016/3040-indicator

2016 Census - Counting Dwellings, Place of Enumeration (SA2)

Data Source: Census of Population and Housing, 2016, TableBuilder

Counting: Dwellings Location on Census Night

Variables:

• HIED Equivalised Total Household Income (weekly)
• RNTRD Rent (weekly) Ranges
• MRERD Mortgage Repayments (monthly) Ranges

library(tidyverse)
library(readxl)
library(sf)
library(tmap)
library(janitor)
library(scales)

Importing the raw data

Transforming the rent data

Transforming the mortgage data

Midpoints for income

Filter to include only bottom 40 per cent of households by income

Equivalised disposable household income - P40 = $714 in 2015-16 for VIC - http://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/6523.02015-16?OpenDocument

Midpoints for rent and mortgages

Joining

mort_join <- left_join(hhincwk_mid40, mort_hh)

Joining, by = "hhincwk"

mort_join <- left_join(mort_join, mortwk_mid)

Joining, by = "mortmth"

mort_join

Stress

What does it look like?

Most of the SA2s have fewer than 30 per cent of households in housing stress.

The shapefile

gm_shp <- st_read("shp/SA2_2016_GMEL.shp") %>% 
  clean_names() %>% 
  select(sa2_name16) %>% 
  left_join(housingstress_sa2, by = c("sa2_name16" = "sa2")) %>% 
  mutate(prop_stress = round(prop_stress, 2))

Reading layer `SA2_2016_GMEL' from data source `/home/williamlai/RScripts/housing_stress_2016/shp/SA2_2016_GMEL.shp' using driver `ESRI Shapefile'
Simple feature collection with 309 features and 12 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 144.3336 ymin: -38.50299 xmax: 145.8784 ymax: -37.1751
proj4string:   +proj=longlat +ellps=GRS80 +no_defs 

The map

Concentrations in the growth areas.